VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL

Currently, vibration-based damage detection is an area of significant research activity. This paper attempts to extend the research in this field through the application of statistical analysis procedures to the vibration-based damage detection problem. The damage detection process is cast in the co...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Mechanical systems and signal processing 2001-07, Vol.15 (4), p.707-721
Hauptverfasser: FUGATE, MICHAEL L., SOHN, HOON, FARRAR, CHARLES R.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 721
container_issue 4
container_start_page 707
container_title Mechanical systems and signal processing
container_volume 15
creator FUGATE, MICHAEL L.
SOHN, HOON
FARRAR, CHARLES R.
description Currently, vibration-based damage detection is an area of significant research activity. This paper attempts to extend the research in this field through the application of statistical analysis procedures to the vibration-based damage detection problem. The damage detection process is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as ‘control charts’ to vibration-based damage detection. First, an autoregressive (AR) model is fit to the measured acceleration–time histories from an undamaged structure. Residual errors, which quantify the difference between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features. Next, the X-bar and S control charts are employed to monitor the mean and variance of the selected features. Control limits for the control charts are constructed based on the features obtained from the initial intact structure. The residual errors computed from the previous AR model and subsequent new data are then monitored relative to the control limits. A statistically significant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state. For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged.
doi_str_mv 10.1006/mssp.2000.1323
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_744713406</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0888327000913232</els_id><sourcerecordid>26700926</sourcerecordid><originalsourceid>FETCH-LOGICAL-c409t-17439603b6b272d1be6059812497a13deb5b0d4a9b78f07d482a6c375387dd843</originalsourceid><addsrcrecordid>eNqFkD1PwzAQhi0EEqWwMmdAMKX4q7YzpmkokUqDmpTVcmxHCko_iFsk_j2O2oEFmE53eu493QPALYIjBCF7XDu3G2EIfUswOQMDBCMWIozYORhAIURIMIeX4Mq5d09FFLIBmLxlk2VcZvkinMRFOg2m8Us8S4NpWqZJPw5WRbaYBUXpoaLMkngevC7zJC2KIMkX5TKfX4OLWrXO3pzqEKye0jJ5Duf5rOdDTWG0DxGnJGKQVKzCHBtUWQbHkUCYRlwhYmw1rqChKqq4qCE3VGDFNOFjIrgxgpIheDjm7rrtx8G6vVw3Ttu2VRu7PTjJKeWI-K88ef8n6TVgRpD4H2Tci8J94ugI6m7rXGdrueuateq-JIKyty97-7K3L3v7fuHulKycVm3dqY1u3I8tRoXAHhNHzHpxn43tpNON3Whrms7qvTTb5rcL3208j-A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>26700926</pqid></control><display><type>article</type><title>VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL</title><source>Access via ScienceDirect (Elsevier)</source><creator>FUGATE, MICHAEL L. ; SOHN, HOON ; FARRAR, CHARLES R.</creator><creatorcontrib>FUGATE, MICHAEL L. ; SOHN, HOON ; FARRAR, CHARLES R.</creatorcontrib><description>Currently, vibration-based damage detection is an area of significant research activity. This paper attempts to extend the research in this field through the application of statistical analysis procedures to the vibration-based damage detection problem. The damage detection process is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as ‘control charts’ to vibration-based damage detection. First, an autoregressive (AR) model is fit to the measured acceleration–time histories from an undamaged structure. Residual errors, which quantify the difference between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features. Next, the X-bar and S control charts are employed to monitor the mean and variance of the selected features. Control limits for the control charts are constructed based on the features obtained from the initial intact structure. The residual errors computed from the previous AR model and subsequent new data are then monitored relative to the control limits. A statistically significant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state. For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged.</description><identifier>ISSN: 0888-3270</identifier><identifier>EISSN: 1096-1216</identifier><identifier>DOI: 10.1006/mssp.2000.1323</identifier><language>eng</language><publisher>London: Elsevier Ltd</publisher><subject>Applied sciences ; Buildings. Public works ; Concrete bridges ; Error analysis ; Exact sciences and technology ; Failure analysis ; Fundamental areas of phenomenology (including applications) ; Industrial metrology. Testing ; Mathematical models ; Measurement and testing methods ; Measurement methods and techniques in continuum mechanics of solids ; Measurements. Technique of testing ; Mechanical engineering. Machine design ; Pattern recognition ; Physics ; Regression analysis ; Solid mechanics ; Statistical process control ; Structural and continuum mechanics</subject><ispartof>Mechanical systems and signal processing, 2001-07, Vol.15 (4), p.707-721</ispartof><rights>2001 Academic Press</rights><rights>2001 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-17439603b6b272d1be6059812497a13deb5b0d4a9b78f07d482a6c375387dd843</citedby><cites>FETCH-LOGICAL-c409t-17439603b6b272d1be6059812497a13deb5b0d4a9b78f07d482a6c375387dd843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1006/mssp.2000.1323$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=1064882$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>FUGATE, MICHAEL L.</creatorcontrib><creatorcontrib>SOHN, HOON</creatorcontrib><creatorcontrib>FARRAR, CHARLES R.</creatorcontrib><title>VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL</title><title>Mechanical systems and signal processing</title><description>Currently, vibration-based damage detection is an area of significant research activity. This paper attempts to extend the research in this field through the application of statistical analysis procedures to the vibration-based damage detection problem. The damage detection process is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as ‘control charts’ to vibration-based damage detection. First, an autoregressive (AR) model is fit to the measured acceleration–time histories from an undamaged structure. Residual errors, which quantify the difference between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features. Next, the X-bar and S control charts are employed to monitor the mean and variance of the selected features. Control limits for the control charts are constructed based on the features obtained from the initial intact structure. The residual errors computed from the previous AR model and subsequent new data are then monitored relative to the control limits. A statistically significant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state. For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged.</description><subject>Applied sciences</subject><subject>Buildings. Public works</subject><subject>Concrete bridges</subject><subject>Error analysis</subject><subject>Exact sciences and technology</subject><subject>Failure analysis</subject><subject>Fundamental areas of phenomenology (including applications)</subject><subject>Industrial metrology. Testing</subject><subject>Mathematical models</subject><subject>Measurement and testing methods</subject><subject>Measurement methods and techniques in continuum mechanics of solids</subject><subject>Measurements. Technique of testing</subject><subject>Mechanical engineering. Machine design</subject><subject>Pattern recognition</subject><subject>Physics</subject><subject>Regression analysis</subject><subject>Solid mechanics</subject><subject>Statistical process control</subject><subject>Structural and continuum mechanics</subject><issn>0888-3270</issn><issn>1096-1216</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqFkD1PwzAQhi0EEqWwMmdAMKX4q7YzpmkokUqDmpTVcmxHCko_iFsk_j2O2oEFmE53eu493QPALYIjBCF7XDu3G2EIfUswOQMDBCMWIozYORhAIURIMIeX4Mq5d09FFLIBmLxlk2VcZvkinMRFOg2m8Us8S4NpWqZJPw5WRbaYBUXpoaLMkngevC7zJC2KIMkX5TKfX4OLWrXO3pzqEKye0jJ5Duf5rOdDTWG0DxGnJGKQVKzCHBtUWQbHkUCYRlwhYmw1rqChKqq4qCE3VGDFNOFjIrgxgpIheDjm7rrtx8G6vVw3Ttu2VRu7PTjJKeWI-K88ef8n6TVgRpD4H2Tci8J94ugI6m7rXGdrueuateq-JIKyty97-7K3L3v7fuHulKycVm3dqY1u3I8tRoXAHhNHzHpxn43tpNON3Whrms7qvTTb5rcL3208j-A</recordid><startdate>20010701</startdate><enddate>20010701</enddate><creator>FUGATE, MICHAEL L.</creator><creator>SOHN, HOON</creator><creator>FARRAR, CHARLES R.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>7TB</scope><scope>H8D</scope><scope>L7M</scope><scope>7TC</scope></search><sort><creationdate>20010701</creationdate><title>VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL</title><author>FUGATE, MICHAEL L. ; SOHN, HOON ; FARRAR, CHARLES R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-17439603b6b272d1be6059812497a13deb5b0d4a9b78f07d482a6c375387dd843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Applied sciences</topic><topic>Buildings. Public works</topic><topic>Concrete bridges</topic><topic>Error analysis</topic><topic>Exact sciences and technology</topic><topic>Failure analysis</topic><topic>Fundamental areas of phenomenology (including applications)</topic><topic>Industrial metrology. Testing</topic><topic>Mathematical models</topic><topic>Measurement and testing methods</topic><topic>Measurement methods and techniques in continuum mechanics of solids</topic><topic>Measurements. Technique of testing</topic><topic>Mechanical engineering. Machine design</topic><topic>Pattern recognition</topic><topic>Physics</topic><topic>Regression analysis</topic><topic>Solid mechanics</topic><topic>Statistical process control</topic><topic>Structural and continuum mechanics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>FUGATE, MICHAEL L.</creatorcontrib><creatorcontrib>SOHN, HOON</creatorcontrib><creatorcontrib>FARRAR, CHARLES R.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Mechanical Engineering Abstracts</collection><jtitle>Mechanical systems and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>FUGATE, MICHAEL L.</au><au>SOHN, HOON</au><au>FARRAR, CHARLES R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL</atitle><jtitle>Mechanical systems and signal processing</jtitle><date>2001-07-01</date><risdate>2001</risdate><volume>15</volume><issue>4</issue><spage>707</spage><epage>721</epage><pages>707-721</pages><issn>0888-3270</issn><eissn>1096-1216</eissn><abstract>Currently, vibration-based damage detection is an area of significant research activity. This paper attempts to extend the research in this field through the application of statistical analysis procedures to the vibration-based damage detection problem. The damage detection process is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as ‘control charts’ to vibration-based damage detection. First, an autoregressive (AR) model is fit to the measured acceleration–time histories from an undamaged structure. Residual errors, which quantify the difference between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features. Next, the X-bar and S control charts are employed to monitor the mean and variance of the selected features. Control limits for the control charts are constructed based on the features obtained from the initial intact structure. The residual errors computed from the previous AR model and subsequent new data are then monitored relative to the control limits. A statistically significant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state. For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged.</abstract><cop>London</cop><pub>Elsevier Ltd</pub><doi>10.1006/mssp.2000.1323</doi><tpages>15</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0888-3270
ispartof Mechanical systems and signal processing, 2001-07, Vol.15 (4), p.707-721
issn 0888-3270
1096-1216
language eng
recordid cdi_proquest_miscellaneous_744713406
source Access via ScienceDirect (Elsevier)
subjects Applied sciences
Buildings. Public works
Concrete bridges
Error analysis
Exact sciences and technology
Failure analysis
Fundamental areas of phenomenology (including applications)
Industrial metrology. Testing
Mathematical models
Measurement and testing methods
Measurement methods and techniques in continuum mechanics of solids
Measurements. Technique of testing
Mechanical engineering. Machine design
Pattern recognition
Physics
Regression analysis
Solid mechanics
Statistical process control
Structural and continuum mechanics
title VIBRATION-BASED DAMAGE DETECTION USING STATISTICAL PROCESS CONTROL
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T10%3A33%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=VIBRATION-BASED%20DAMAGE%20DETECTION%20USING%20STATISTICAL%20PROCESS%20CONTROL&rft.jtitle=Mechanical%20systems%20and%20signal%20processing&rft.au=FUGATE,%20MICHAEL%20L.&rft.date=2001-07-01&rft.volume=15&rft.issue=4&rft.spage=707&rft.epage=721&rft.pages=707-721&rft.issn=0888-3270&rft.eissn=1096-1216&rft_id=info:doi/10.1006/mssp.2000.1323&rft_dat=%3Cproquest_cross%3E26700926%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=26700926&rft_id=info:pmid/&rft_els_id=S0888327000913232&rfr_iscdi=true